Setup
Load required libraries for wrangling data, charting, and mapping
library(plyr,quietly = T) # data wrangling
library(dplyr,quietly = T) # data wrangling
library(ggplot2, quietly = T) # charting
library(ggthemes,quietly = T) # so I can add the highcharts theme and palette
library(scales,quietly = T) # to load the percent function when labeling plots
library(caret,quietly = T) # classification and regression training
library(foreach,quietly = T) # parallel processing to speed up the model training
library(doMC,quietly = T) # parallel processing to speed up the model training
library(lubridate,quietly = T) # for timing models
Set our preferred charting theme
theme_set(theme_minimal()+theme_hc()+theme(legend.key.width = unit(1.5, "cm")))
Run script to get hunter data
source('~/_code/colorado-dow/datasets/Colorado Elk Harvest Data.R', echo=F)
The working directory was changed to /Users/psarnow/_code/colorado-dow/datasets inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
Table of the harvest data
head(COElkRifleAll)
Unit Harvest.Antlered Hunters.Antlered Success.Antlered Season HuntCode Harvest.Antlerless Hunters.Antlerless Success.Antlerless Hunters.Either Success.Either Year
1 1 0 0 NA 1 EM001O1R NA NA NA NA NA 2006
2 2 0 0 NA 1 EM002O1R NA NA NA NA NA 2006
3 201 0 0 NA 1 EM201O1R NA NA NA NA NA 2006
4 3 0 0 NA 1 EM003O1R NA NA NA NA NA 2006
5 301 0 0 NA 1 EM301O1R NA NA NA NA NA 2006
6 4 0 0 NA 1 EM004O1R NA NA NA NA NA 2006
Run script to get draw data
source('~/_code/colorado-dow/datasets/Elk Drawing Summaries.R', echo=F)
Expected 26 pieces. Missing pieces filled with `NA` in 85 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].binding character and factor vector, coercing into character vectorExpected 26 pieces. Missing pieces filled with `NA` in 85 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].binding character and factor vector, coercing into character vectorExpected 26 pieces. Missing pieces filled with `NA` in 85 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].binding character and factor vector, coercing into character vectorExpected 26 pieces. Missing pieces filled with `NA` in 81 rows [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, ...].binding character and factor vector, coercing into character vectorThe working directory was changed to /Users/psarnow/_code/colorado-dow/datasets inside a notebook chunk. The working directory will be reset when the chunk is finished running. Use the knitr root.dir option in the setup chunk to change the working directory for notebook chunks.
Table of the data
head(COElkDrawAll)
HuntCode Orig_Quota Ttl_Chce_1 Chcs_Drawn Sex Unit Season Year Draw_Success
1 EE003O1R 1075 2188 1075 Either 3 1 2006 0.4913163
2 EE003O4R 300 533 300 Either 3 4 2006 0.5628518
3 EE004O4R 150 235 150 Either 4 4 2006 0.6382979
4 EE005O4R 10 33 10 Either 5 4 2006 0.3030303
5 EE006O1R 100 168 100 Either 6 1 2006 0.5952381
6 EE006O4R 80 83 80 Either 6 4 2006 0.9638554
source geodata
source('~/_code/colorado-dow/datasets/Colorado GMUnit and Road data.R', echo=F)
OGR data source with driver: ESRI Shapefile
Source: "/Users/psarnow/_code/colorado-dow/datasets/CPW_GMUBoundaries/BigGameGMUBoundaries03172015.shp", layer: "BigGameGMUBoundaries03172015"
with 185 features
It has 12 fields
Integer64 fields read as strings: GMUID
OGR data source with driver: ESRI Shapefile
Source: "/Users/psarnow/_code/colorado-dow/datasets/ne_10m_roads/ne_10m_roads.shp", layer: "ne_10m_roads"
with 56601 features
It has 29 fields
Integer64 fields read as strings: scalerank question
Take a peak at the boundary data
head(Unitboundaries2)
id long lat order hole piece group COUNTY DEERDAU ELKDAU ANTDAU MOOSEDAU BEARDAU LIONDAU SQ_MILES ACRES SHAPE_area SHAPE_len Unit
1 1 -109.0486 40.83476 1 FALSE 1 1.1 MOFFAT D-1 E-47 A-11 M-99 B-15 L-1 127.2227 81422.53 329506619 100751.7 1
2 1 -109.0472 40.83357 2 FALSE 1 1.1 MOFFAT D-1 E-47 A-11 M-99 B-15 L-1 127.2227 81422.53 329506619 100751.7 1
3 1 -109.0460 40.83295 3 FALSE 1 1.1 MOFFAT D-1 E-47 A-11 M-99 B-15 L-1 127.2227 81422.53 329506619 100751.7 1
4 1 -109.0449 40.83228 4 FALSE 1 1.1 MOFFAT D-1 E-47 A-11 M-99 B-15 L-1 127.2227 81422.53 329506619 100751.7 1
5 1 -109.0438 40.83204 5 FALSE 1 1.1 MOFFAT D-1 E-47 A-11 M-99 B-15 L-1 127.2227 81422.53 329506619 100751.7 1
6 1 -109.0423 40.83181 6 FALSE 1 1.1 MOFFAT D-1 E-47 A-11 M-99 B-15 L-1 127.2227 81422.53 329506619 100751.7 1
Set to predictive analytics directory
setwd("~/_code/colorado-dow/phase III - predictive analytics")
Organize data
Will start by grouping all of the seasons together, and modeling the number of hunters per Year and Unit
Group Draw results data by Year and Unit
COElkDraw_Unit <- summarise(group_by(COElkDrawAll,Year,Unit,Season),
Quota = sum(Orig_Quota,na.rm = T),
Drawn = sum(Chcs_Drawn,na.rm = T))
Appropriate field classes for model training
COElkDraw_Unit$Year <- as.numeric(COElkDraw_Unit$Year)
Group Hunter data by Year and Unit
COElkHunters_Unit <- summarise(group_by(COElkRifleAll,Year,Unit,Season),
Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T))
COElkHunters_Unit$Year <- as.numeric(COElkHunters_Unit$Year)
Join in Hunter and Draw data together
COElkHunters_Unit <- left_join(COElkHunters_Unit, COElkDraw_Unit, by = c("Year","Unit","Season"))
Replace the draw data that don’t have entries with 0
COElkHunters_Unit$Drawn[is.na(COElkHunters_Unit$Drawn)] <- 0
COElkHunters_Unit$Quota[is.na(COElkHunters_Unit$Quota)] <- 0
Split into train and test sets. Will use 75% of the data to train on.
COElkHunters_Unit <- mutate(group_by(COElkHunters_Unit,Unit),
numentries = n())
COElkHunters_Unit <- filter(COElkHunters_Unit, numentries >= 3)
COElkHunters_Unit$UnitYearSeason <- paste(COElkHunters_Unit$Unit, COElkHunters_Unit$Year,COElkHunters_Unit$Season)
traindata2 <- COElkHunters_Unit %>% group_by(Unit) %>% sample_frac(size = .75, replace = F)
testdata2 <- COElkHunters_Unit[!COElkHunters_Unit$UnitYearSeason %in% traindata2$UnitYearSeason,]
COElkHunters_Unit <- select(COElkHunters_Unit, -UnitYearSeason, -numentries)
traindata2 <- select(traindata2, -UnitYearSeason, -numentries)
testdata2 <- select(testdata2, -UnitYearSeason, -numentries)
Save off for importing into AzureML
Data Visualization
notice that the number of hunters data is skewed.
ggplot(COElkHunters_Unit, aes(Hunters)) +
geom_density() +
xlab("Hunters in Unit") +
ylab("Number of Units") +
theme(axis.text.y = element_blank()) +
labs(title="Distribution of Hunters in each Unit", subtitle="2006-2017", caption="source: cpw.state.co.us")

A general rule of thumb to consider is that skewed data whose ratio of the highest value to the lowest value is greater than 20 have significant skewness. Also, the skewness statistic can be used as a diagnostic. If the predictor distribution is roughly symmetric, the skewness values will be close to zero. As the distribution becomes more right skewed, the skewness statistic becomes larger. Similarly, as the distribution becomes more left skewed, the value becomes negative. Replacing the data with the log, square root, or inverse may help to remove the skew.
Example of how BoxCox can redistribute the data
preProcValues2 <- preProcess(as.data.frame(traindata2), method = "BoxCox")
trainBC <- predict(preProcValues2, as.data.frame(traindata2))
ggplot(trainBC, aes(Hunters)) +
geom_density() +
xlab("BoxCox Hunters in Unit") +
ylab("Number of Units") +
theme(axis.text.y = element_blank()) +
labs(title="BoxCox Distribution of Hunters in each Unit", subtitle="2006-2017", caption="source: cpw.state.co.us")
caret has a preproccess function for correcting for skewness ‘BoxCox’, we will need to be sure to look at using this function in the training models.
Model Building
Model Training Methods
Loop through possible methods, utilizing the quicker ‘adaptive_cv’ parameter search from caret. Consider scripting this into AzureML to make it run much faster, though there is more setup and errors to control for
quickmethods <- c("lm",'svmLinear',"svmRadial","knn","cubist","kknn","glm.nb")
step1_all_Unit <- NULL
for (imethod in quickmethods) {
step1_Unit <- NULL
start <- now()
# if (imethod == "lm") {
# controlmethod <- "repeatedcv"
# } else {controlmethod <- "adaptive_cv"}
controlmethod <- "repeatedcv"
fitControl <- trainControl(
method = controlmethod,
# search = 'random',
number = 4,
repeats = 4,
allowParallel = TRUE,
summaryFunction = defaultSummary)
registerDoSEQ()
registerDoMC(cores = 6)
HuntersModel_1_Unit = train(Hunters ~ ., data = traindata2,
method = imethod,
tuneLength = 15,
trControl = fitControl)
HuntersModel_1_Unit
# measure performance
predictdata <- predict(HuntersModel_1_Unit, testdata2)
step1_Unit$method <- imethod
step1_Unit$RMSE <- postResample(pred = predictdata, obs = testdata2$Hunters)[1]
step1_Unit$duration <- now() - start
step1_Unit <- as.data.frame(step1_Unit)
step1_all_Unit <- rbind(step1_all_Unit,step1_Unit)
}
Timing stopped at: 0.033 0.007 0.04
View Results, and compare to previous first models that did not expand to Seasons
step1_all
method RMSE duration
RMSE lm 165.0025 6.866598 secs
RMSE1 svmLinear 178.0942 4.950974 secs
RMSE2 svmRadial 134.0561 22.081016 secs
RMSE3 knn 733.8210 10.502875 secs
RMSE4 cubist 151.9572 3.057477 secs
RMSE5 kknn 132.0211 9.778734 secs
RMSE6 cubist 160.5464 1.027274 secs
step1_all_Unit
method RMSE duration
RMSE lm 153.05547 4.228410 secs
RMSE1 svmLinear 162.67004 35.926092 secs
RMSE2 svmRadial 62.73800 11.961980 secs
RMSE3 knn 70.93077 2.446104 secs
RMSE4 cubist 68.93277 4.504099 secs
RMSE5 kknn 58.81122 7.604784 secs
RMSE6 glm.nb 164.19801 25.610913 secs
The performance RMSE metric is certainly improved when including the Seasonal grouping. Lets take the best method, and see if it is visually reasonable while also charting without Seasons.
Predictions using best Season model
Build model
controlmethod <- "repeatedcv"
fitControl <- trainControl(
method = controlmethod,
# search = 'random',
number = 4,
repeats = 4,
allowParallel = TRUE,
summaryFunction = defaultSummary)
registerDoSEQ()
registerDoMC(cores = 6)
HuntersModel_1_Unit = train(Hunters ~ ., data = COElkHunters_Unit,
method = "kknn",
# preProc = c("center","scale"),
tuneLength = 15,
trControl = fitControl)
Predict Hunter for next year, 2018
COElkHunters2018_Unit <- COElkHunters_Unit
COElkHunters2018_Unit$Unit_Season <- paste(COElkHunters2018_Unit$Unit,COElkHunters2018_Unit$Season)
COElkHunters2018_Unit <- as.data.frame(unique(COElkHunters2018_Unit$Unit_Season))
colnames(COElkHunters2018_Unit) <- "Unit_Season"
# Fill in missing Units and Seasons per unique Unit_Seasons
COElkHunters2018_Unit$Unit <- str_extract(COElkHunters2018_Unit$Unit_Season,"[:alnum:]+(?=[:blank:])")
COElkHunters2018_Unit$Season <- str_extract(COElkHunters2018_Unit$Unit_Season,"(?<=[:blank:])[:alnum:]+")
COElkHunters2018_Unit <- select(COElkHunters2018_Unit, -Unit_Season)
COElkHunters2018_Unit$Year <- 2018
# Draw data for 2018
COElkDraw_Unit2018 <- filter(COElkDraw_Unit,Year==2018)
# A left join will autofill missing draw data with NAs, but will retain the full list of Unit Seasons
COElkHunters2018_Unit <- left_join(COElkHunters2018_Unit,COElkDraw_Unit2018)
Joining, by = c("Unit", "Season", "Year")
# Replace the draw data that don't have entries with 0
COElkHunters2018_Unit$Drawn[is.na(COElkHunters2018_Unit$Drawn)] <- 0
COElkHunters2018_Unit$Quota[is.na(COElkHunters2018_Unit$Quota)] <- 0
# Only use the fields that were included in the model
COElkHunters2018_Unit <- COElkHunters2018_Unit[, colnames(COElkHunters2018_Unit) %in% c("Unit","Season",HuntersModel_1_Unit$coefnames)]
# Use trained model to predict Hunters
COElkHunters2018_Unit$Hunters <- round(predict(HuntersModel_1_Unit, COElkHunters2018_Unit))
COElkHunters2018_Unit$Hunters[COElkHunters2018_Unit$Hunters<0] <- 0
Label and Join models together for comparisons
Hunterscompare <- rbind(Hunterscompare,Hunterscompare_Season)
Error in bind_rows_(x, .id) : Argument 1 must have names
# Group Units
HunterscompareStatewide <- summarise(group_by(Hunterscompare,Year,modeldata),
Hunters = sum(Hunters))
ggplot(HunterscompareStatewide, aes(Year,Hunters,group=modeldata,fill=modeldata)) +
geom_bar(position="dodge",stat="identity") +
coord_cartesian(ylim = c(130000,155000)) +
scale_fill_hc() +
labs(title="Statewide Elk Hunters", caption="source: cpw.state.co.us")

Hunters Statewide by Season and Year
Hunters_Season <- rbind.fill(COElkHunters_Unit,COElkHunters2018_Unit)
ggplot(Hunters_Season, aes(Year,Hunters,group=Season,fill=Season)) +
geom_bar(position="dodge",stat="identity") +
# coord_cartesian(ylim = c(130000,155000)) +
scale_fill_hc() +
labs(title="Statewide Elk Hunters", caption="source: cpw.state.co.us")

Hunters …. other charts?
Keep going by creating models for hunters per year, unit and season?
top_two_models <- top_n(step1_all,2,-RMSE)$method
More Model Training Methods
Take the top two and determine some additonal methods to try by maximizing the Jaccard dissimilarity between sets of models
tag <- read.csv("tag_data.csv", row.names = 1)
tag <- as.matrix(tag)
Select only models for regression
regModels <- tag[tag[,"Regression"] == 1,]
all <- 1:nrow(regModels)
dissimilarmethods_all <- NULL
for (itoptwo in 1:2) {
## Seed the analysis with the model of interest
start <- grep(top_two_models[itoptwo], rownames(regModels), fixed = TRUE)
pool <- all[all != start]
## Select 4 model models by maximizing the Jaccard
## dissimilarity between sets of models
nextMods <- maxDissim(regModels[start,,drop = FALSE],
regModels[pool, ],
method = "Jaccard",
n = 4)
rownames(regModels)[c(nextMods)]
dissimilarmethods <- rownames(regModels)[nextMods]
dissimilarmethods <- str_extract(string = dissimilarmethods,pattern = "[:alnum:]+(?=\\))")
dissimilarmethods_all <- c(dissimilarmethods_all,dissimilarmethods)
}
Now we have 8 more methods to try in the same manner
dissimilarmethods_all <- unique(dissimilarmethods_all)
dissimilarmethods_all
for (imethod in dissimilarmethods_all) {
step1 <- NULL
start_timer <- now()[1]
if (imethod == "lm") {
controlmethod <- "repeatedcv"
} else {controlmethod <- "adaptive_cv"}
fitControl <- trainControl(
method = controlmethod,
# search = 'random',
number = 4,
repeats = 4,
allowParallel = TRUE,
summaryFunction = defaultSummary)
registerDoSEQ()
registerDoMC(cores = 6)
HuntersModel_1 = train(Hunters ~ ., data = traindata,
method = imethod,
preProc = c("center","scale"),
tuneLength = 15,
trControl = fitControl)
HuntersModel_1
# measure performance
predictdata <- predict(HuntersModel_1, testdata)
step1$method <- imethod
step1$RMSE <- postResample(pred = predictdata, obs = testdata$Hunters)[1]
step1$duration <- now()[1] - start_timer[1]
step1 <- as.data.frame(step1)
step1_all <- rbind(step1_all,step1)
}
step1_all
Preprocessing on Top Modeling Methods
Now lets work on some refined tuning on the top methods Any valuable preprocessing steps?
preprocessfunctions <- c("BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica", "spatialSign", "corr", "zv", "nzv")
topmethods <- top_n(step1_all,2,-RMSE)$method
fitControl <- trainControl(
method = "adaptive_cv", #repeatedcv,
search = 'random',
number = 10, #4
repeats = 10, #10
# classProbs = TRUE,
# savePred = TRUE,
allowParallel = TRUE,
summaryFunction = defaultSummary)
PPperformance_all <- NULL
PPperformance <- NULL
for (imethod in topmethods) {
for (ipreprocess in preprocessfunctions) {
registerDoSEQ()
registerDoMC(cores = 6)
PreProcessModel = train(Hunters ~ ., data = traindata,
method = imethod,
preProc = ipreprocess,
#tuneLength = 10,
#tuneGrid = kknnTuneGrid,
trControl = fitControl)
print(PreProcessModel)
# check performance
predictdata <- predict(PreProcessModel, testdata)
PPperformance$method <- imethod
PPperformance$preprocess <- ipreprocess
PPperformance$RMSE <- postResample(pred = predictdata, obs = testdata$Hunters)[1]
PPperformance <- as.data.frame(PPperformance)
PPperformance_all <- rbind(PPperformance_all,PPperformance)
}
}
# Some of the models were loaded into AzureML and processed there.
# Output from AzureML
# [ModuleOutput] method preprocess RMSE
# [ModuleOutput] RMSE kknn BoxCox 130.7939
# [ModuleOutput] RMSE1 kknn YeoJohnson 130.9600
# [ModuleOutput] RMSE2 kknn center 130.7331
# [ModuleOutput] RMSE3 kknn scale 130.1818
# [ModuleOutput] RMSE4 kknn pca 130.2071
# [ModuleOutput] RMSE5 svmRadial BoxCox 154.0898
# [ModuleOutput] RMSE6 svmRadial YeoJohnson 169.9816
# [ModuleOutput] RMSE7 svmRadial center 154.1891
# [ModuleOutput] RMSE8 svmRadial scale 154.1000
# [ModuleOutput] RMSE9 svmRadial pca 164.0881
# svmRadial and kknn don't perform better with any of the preprocessing functions in place
PPperformance_all
Model Predictors
Now we can review the predictors, there are only a few fields so I will manually test performance while excluding each of them to monitor their importance. Some of our fields are instinctively required (Year, Unit)
Predictors <- c("Quota","Drawn")
Predictorperformance_all <- NULL
Predictorperformance <- NULL
for (imethod in topmethods) {
for (ipredictor in Predictors) {
registerDoSEQ()
registerDoMC(cores = 6)
PredictorModel = train(Hunters ~ ., data = select(traindata,-ipredictor),
method = imethod,
tuneLength = 15,
trControl = fitControl)
print(PredictorModel)
# check performance
predictdata <- predict(PredictorModel, testdata)
Predictorperformance$method <- imethod
Predictorperformance$missing_predictor <- ipredictor
Predictorperformance$RMSE <- postResample(pred = predictdata, obs = testdata$Hunters)[1]
Predictorperformance <- as.data.frame(Predictorperformance)
Predictorperformance_all <- rbind(Predictorperformance_all,Predictorperformance)
}
}
Predictorperformance_all
svMRadial will perform better with all of the predictors, while kknn performs better with only Unit and Year fields
Use above information to test out various combinations of preprocessing and predictor sets
kknn
kknn without Quota and Drawn
fitControl <- trainControl(
method = "adaptive_cv", #repeatedcv,
search = 'random',
number = 10, #4
repeats = 10, #10
# classProbs = TRUE,
# savePred = TRUE,
allowParallel = TRUE,
summaryFunction = defaultSummary)
registerDoSEQ()
registerDoMC(cores = 6)
kknnModel = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
method = "kknn",
tuneLength = 75,
trControl = fitControl)
kknnModel
Best RMSE
# not sure why caret is selecting parameters with higher RMSE, lets select manually
RSMEkknn <- filter(kknnModel$results, RMSE == min(RMSE))
RSMEkknn$kernel <- as.character(RSMEkknn$kernel)
RSMEkknn
Model Tuning
run again with a tune grid
kknnTuneGrid <- data.frame(kmax = c(RSMEkknn$kmax,RSMEkknn$kmax,RSMEkknn$kmax,RSMEkknn$kmax,RSMEkknn$kmax),
distance = c(RSMEkknn$distance*.7,RSMEkknn$distance*.9,RSMEkknn$distance,RSMEkknn$distance*1.1,RSMEkknn$distance*1.3),
kernel = c(RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel))
fitControl <- trainControl(
method = "repeatedcv", #repeatedcv,
number = 10, #4
repeats = 10, #10
allowParallel = TRUE,
summaryFunction = defaultSummary)
registerDoSEQ()
registerDoMC(cores = 6)
kknnGridModel = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
method = "kknn",
tuneGrid = kknnTuneGrid,
trControl = fitControl)
kknnGridModel
Best RMSE
RSMEkknn <- filter(kknnGridModel$results, RMSE == min(RMSE))
RSMEkknn$kernel <- as.character(RSMEkknn$kernel)
run again with a tune grid
kknnTuneGrid2 <- data.frame(kmax = c(RSMEkknn$kmax*.7,RSMEkknn$kmax*.9,RSMEkknn$kmax,RSMEkknn$kmax*1.1,RSMEkknn$kmax*1.3),
distance = c(RSMEkknn$distance,RSMEkknn$distance,RSMEkknn$distance,RSMEkknn$distance,RSMEkknn$distance),
kernel = c(RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel))
registerDoSEQ()
registerDoMC(cores = 6)
kknnGridModel2 = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
method = "kknn",
tuneGrid = kknnTuneGrid2,
trControl = fitControl)
kknnGridModel2
One more time on final parameter (kernel) Best RMSE
RSMEkknn <- filter(kknnGridModel2$results, RMSE == min(RMSE))[1,]
kernels <- levels(kknnModel$results$kernel)
run again with a tune grid
kknnTuneGrid3 <- data.frame(kmax = rep(465.0,8),
distance = rep(0.1395586,8),
kernel = kernels)
registerDoSEQ()
registerDoMC(cores = 6)
kknnGridModel3 = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
method = "kknn",
tuneGrid = kknnTuneGrid3,
trControl = fitControl)
kknnGridModel3
RSMEkknn <- filter(kknnGridModel3$results, RMSE == min(RMSE))
Best RMSE for kknn thus far
RSMEkknn <- filter(kknnModel$results, RMSE == min(RMSE))
Work thru some resampling methods with best kknn params
kknnTuneGrid4 <- data.frame(kmax = RSMEkknn$kmax,
distance = RSMEkknn$distance,
kernel = as.character(RSMEkknn$kernel))
trainmethods <- c("boot", "boot632", "optimism_boot", "boot_all", "cv", "repeatedcv", "LOOCV", "LGOCV", "none")
trainmethodperformance_all <- NULL
for (itrainmethod in trainmethods) {
trainmethodperformance <- NULL
fitControl <- trainControl(
method = itrainmethod,
number = 10, #4
repeats = 10, #10
allowParallel = TRUE,
summaryFunction = defaultSummary)
registerDoSEQ()
registerDoMC(cores = 6)
kknnTrainModel = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
method = "kknn",
tuneGrid = kknnTuneGrid4,
trControl = fitControl)
print(kknnTrainModel)
trainmethodperformance <- filter(kknnTrainModel$results, RMSE == min(RMSE))
trainmethodperformance$trainmethod <- itrainmethod
trainmethodperformance_all <- rbind.fill(trainmethodperformance_all,trainmethodperformance)
}
trainmethodperformance_all
fitControl <- trainControl(
method = "optimism_boot",
number = 10, #4
allowParallel = TRUE,
summaryFunction = defaultSummary)
kknnFinalTrainModel = train(Hunters ~ ., data = COElkHunters,
method = "kknn",
tuneGrid = kknnTuneGrid4,
trControl = fitControl)
save off for future loading
save(kknnFinalTrainModel, file = "~/_code/colorado-dow/datasets/kknnFinalTrainModel.RData")
run again with a tune grid
svmRadTuneGrid <- data.frame(.sigma = c(0.0037653,0.0037653,0.0037653,0.0037653,0.0037653),
.C = c(2048*.7,2048*.9,2048,2048*1.1,2048*1.3))
fitControl <- trainControl(
method = "repeatedcv", #repeatedcv,
number = 10, #4
repeats = 10, #10
allowParallel = TRUE,
summaryFunction = defaultSummary)
registerDoSEQ()
registerDoMC(cores = 6)
svmRadGridModel = train(Hunters ~ ., data = COElkHunters,
method = "svmRadial",
tuneGrid = svmRadTuneGrid,
trControl = fitControl)
svmRadGridModel
Best RMSE, not sure why caret is selecting parameters with higher RMSE, lets select manually
RSMEsvmRad <- filter(svmRadGridModel$results, RMSE == min(RMSE))
run again with a tune grid
svmRadTuneGrid2 <- data.frame(.sigma = c(RSMEsvmRad$sigma*.7,RSMEsvmRad$sigma*.9,RSMEsvmRad$sigma,RSMEsvmRad$sigma*1.1,RSMEsvmRad$sigma*1.3),
.C = c(RSMEsvmRad$C,RSMEsvmRad$C,RSMEsvmRad$C,RSMEsvmRad$C,RSMEsvmRad$C))
registerDoSEQ()
registerDoMC(cores = 6)
svmRadGridModel2 = train(Hunters ~ ., data = COElkHunters,
method = "svmRadial",
tuneGrid = svmRadTuneGrid2,
trControl = fitControl)
svmRadGridModel2
RSMEsvmRad <- filter(svmRadGridModel2$results, RMSE == min(RMSE))
Work thru some resampling methods with best kknn params
svmRadTuneGrid3 <- data.frame(.sigma = RSMEsvmRad$sigma,
.C = RSMEsvmRad$C)
trainmethods <- c("boot", "boot632", "optimism_boot", "cv", "repeatedcv", "LOOCV", "LGOCV", "none")
trainmethodperformance_all <- NULL
for (itrainmethod in trainmethods) {
trainmethodperformance <- NULL
fitControl <- trainControl(
method = itrainmethod,
number = 10, #4
repeats = 10, #10
allowParallel = TRUE,
summaryFunction = defaultSummary)
registerDoSEQ()
registerDoMC(cores = 6)
svmRadTrainModel = train(Hunters ~ ., data = COElkHunters,
method = "svmRadial",
tuneGrid = svmRadTuneGrid3,
trControl = fitControl)
print(svmRadTrainModel)
trainmethodperformance <- filter(svmRadTrainModel$results, RMSE == min(RMSE))
trainmethodperformance$trainmethod <- itrainmethod
trainmethodperformance_all <- rbind.fill(trainmethodperformance_all,trainmethodperformance)
}
trainmethodperformance_all
fitControl <- trainControl(
method = "optimism_boot",
number = 10, #4
allowParallel = TRUE,
summaryFunction = defaultSummary)
svmRadFinalTrainModel = train(Hunters ~ ., data = COElkHunters,
method = "svmRadial",
tuneGrid = svmRadTuneGrid3,
trControl = fitControl)
save off for future loading
save(svmRadFinalTrainModel, file = "~/_code/colorado-dow/datasets/svmRadFinalTrainModel.RData")
back to train vs test data for one more performance measure and chart… even though for future data we will use the final trained model
svmRadTrainModel = train(Hunters ~ ., data = traindata,
method = "svmRadial",
tuneGrid = svmRadTuneGrid3,
trControl = fitControl)
check performance
predictdata <- predict(svmRadTrainModel, testdata)
postResample(pred = predictdata, obs = testdata$Hunters)
Chart performance of predicted
chartperformance <- data.frame(predicted = predictdata, observed = testdata$Hunters)
ggplot(chartperformance, aes(predicted,observed)) +
geom_point() +
labs(title="Performance of Number of Hunters Prediction", caption="source: cpw.state.co.us")
kknn performed better than svmRadial RMSE=130 vs 154
FinalHuntersmodel <- kknnFinalTrainModel
# FinalHuntersmodel <- svmRadFinalTrainModel
save(FinalHuntersmodel, file = "~/_code/colorado-dow/datasets/FinalHuntersmodel.RData")
Use the 2018 Draw data to predict the number of hunters in 2018
COElkDraw2018 <- filter(COElkDraw, Year == 2018)
COElkHunters2018 <- COElkDraw2018[, colnames(COElkDraw2018) %in% c("Unit",FinalHuntersmodel$coefnames)]
COElkHunters2018 <- as.data.frame(unique(COElkHunters$Unit))
colnames(COElkHunters2018) <- "Unit"
COElkHunters2018$Year <- 2018
COElkHunters2018 <- left_join(COElkHunters2018,filter(COElkDraw,Year==2018))
# Replace the draw data that don't have entries with 0
COElkHunters2018$Drawn[is.na(COElkHunters2018$Drawn)] <- 0
COElkHunters2018$Quota[is.na(COElkHunters2018$Quota)] <- 0
COElkHunters2018 <- COElkHunters2018[, colnames(COElkHunters2018) %in% c("Unit",FinalHuntersmodel$coefnames)]
COElkHunters2018$Hunters <- round(predict(FinalHuntersmodel, COElkHunters2018))
COElkHunters2018$Hunters[COElkHunters2018$Hunters<0] <- 0
Save off so we don’t have to recreate the model everytime we want the results
save(COElkHunters2018,file="COElkHunters2018.RData")
Total Elk Harvest
Statewide
Group seasons
COElkHuntersStatewide <- summarise(group_by(COElkRifleAll,Year,Unit),
Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T))
COElkHunters2018b <- COElkHunters2018
# COElkHunters2018b$Year <- as.character(COElkHunters2018b$Year)
# Join 2018 to historic data
COElkHuntersAll <- rbind.fill(COElkHuntersStatewide,COElkHunters2018b)
# Group Units
COElkHuntersStatewide <- summarise(group_by(COElkHuntersAll,Year),
Hunters = sum(Hunters))
ggplot(COElkHuntersStatewide, aes(Year,Hunters)) +
geom_bar(stat="identity",fill=ggthemes_data$hc$palettes$default[2]) +
coord_cartesian(ylim = c(130000,155000)) +
labs(title="Statewide Elk Hunters", caption="source: cpw.state.co.us")
TODO commentary
Hunters by Unit
I’d like to know where the hunters are distributed across the state.
Next year’s data
Year2018 <- filter(COElkHuntersAll, Year == "2018")
HunterstoPlot <- left_join(Unitboundaries2,Year2018, by=c("Unit"))
ggplot(HunterstoPlot, aes(long, lat, group = group)) +
geom_polygon(aes(fill = Hunters),colour = "grey50", size = .2) + #Unit boundaries
geom_path(data = COroads,aes(x = long, y = lat, group = group), color="#3878C7",size=2) + #Roads
geom_text(data=data_centroids,aes(x=longitude,y=latitude,label = Unit),size=3) + #Unit labels
scale_fill_distiller(palette = "Oranges",direction = 1,na.value = 'light grey') +
xlab("") + ylab("") +
labs(title="Predicted 2018 Colorado Elk Hunters", caption="source: cpw.state.co.us")
TODO - commentary
Year to Year Hunter Trends
Create a png of each year
icounter <- 0
for (imap in unique(COElkHuntersAll$Year)){
# Colorado aspect ratio = 1087w x 800h -> 1.35875
# Use trial and error to determine which width and height to define for png files that will retain the correct aspect ratio
png(file=paste("HuntersMap",imap,".png"), width=948, height=700)
yearplot <- filter(COElkHuntersAll, Year == imap)
HunterstoPlot <- left_join(Unitboundaries2,yearplot, by=c("Unit"))
p1 <- ggplot(HunterstoPlot, aes(long, lat, group = group)) +
geom_polygon(aes(fill = Hunters),colour = "grey50", size = .2) + #Unit boundaries
geom_path(data = COroads,aes(x = long, y = lat, group = group), color="#3878C7",size=2) + #Roads
geom_text(data=data_centroids,aes(x=longitude,y=latitude,label = Unit),size=5) + #Unit labels
scale_fill_distiller(palette = "Oranges",
direction = 1,
na.value = 'light grey',
limits = c(0,max(COElkHuntersAll$Hunters))) + #fix so each year chart has same color breaks
xlab("") + ylab("") +
theme(plot.title=element_text(hjust = .5)) +
theme(plot.subtitle=element_text(hjust = icounter/length(unique(COElkHuntersAll$Year)))) +
labs(title="Colorado Elk Hunters", subtitle=imap, caption="source: cpw.state.co.us")
plot(p1)
dev.off()
icounter <- icounter + 1
}
Convert the .png files to one .gif file using ImageMagick.
system("convert -delay 150 *.png HuntersmapPred.gif")
> TODO - commentary
Remove the .png files
file.remove(list.files(pattern=".png"))
Number of Hunters Rank of the Units
Would also be beneficial to rank each unit so I can reference later. In this case average the number of hunters of the last few years
HunterRank2018 <- filter(COElkHuntersAll, as.numeric(Year) == 2018)
HunterRank2018 <- summarise(group_by(HunterRank2018,Unit),
Hunters = mean(Hunters,na.rm = T))
HunterRank2018$HuntersRank = rank(-HunterRank2018$Hunters)
HunterRank2018 <- filter(HunterRank2018, HuntersRank <= 50) # top 50 units
# In order for the chart to retain the order of the rows, the X axis variable (i.e. the categories) has to be converted into a factor.
HunterRank2018 <- HunterRank2018[order(-HunterRank2018$Hunters), ] # sort
HunterRank2018$Unit <- factor(HunterRank2018$Unit, levels = HunterRank2018$Unit) # to retain the order in plot.
Lollipop Chart
ggplot(HunterRank2018, aes(x=Unit, y=Hunters)) +
geom_point(size=3) +
geom_segment(aes(x=Unit,
xend=Unit,
y=0,
yend=Hunters)) +
labs(title="Predicted Elk Hunters 2018\nTop 50 Units", subtitle="Hunters by Unit", caption="source: cpw.state.co.us")
TODO - commentary
---
title: "Predict Number of Future Elk Hunters per Unit Season"
author: "Pierre Sarnow"
output:
  html_notebook:
    toc: yes
    toc_float: false
    toc_depth: 6
    theme: yeti
    hightlight: default
    code_folding: none
---


***
## Description
Use historical draw results, and number of hunters to train a model we can use to 
predict the number of hunters in future years. I would like to compare this to the results
we generated by grouping the seasons together. 


*__NOTICE__ that I am only looking at the general rifle hunting seasons on public land. There are also 
hunters for Archery, Muzzleloader, Private Land, Ranching for Wildlife, etc.*

***
## Setup
Load required libraries for wrangling data, charting, and mapping
```{r}
library(plyr,quietly = T) # data wrangling
library(dplyr,quietly = T) # data wrangling
library(ggplot2, quietly = T) # charting
library(ggthemes,quietly = T) # so I can add the highcharts theme and palette
library(scales,quietly = T) # to load the percent function when labeling plots
library(caret,quietly = T) # classification and regression training
library(foreach,quietly = T) # parallel processing to speed up the model training
library(doMC,quietly = T) # parallel processing to speed up the model training
library(lubridate,quietly = T) # for timing models
```

Set our preferred charting theme
```{r}
theme_set(theme_minimal()+theme_hc()+theme(legend.key.width = unit(1.5, "cm")))
``` 

Run script to get hunter data
```{r}
source('~/_code/colorado-dow/datasets/Colorado Elk Harvest Data.R', echo=F)
```

Table of the harvest data
```{r}
head(COElkRifleAll)
```


Run script to get draw data
```{r}
source('~/_code/colorado-dow/datasets/Elk Drawing Summaries.R', echo=F)
```

Table of the data
```{r}
head(COElkDrawAll)
```

source geodata
```{r}
source('~/_code/colorado-dow/datasets/Colorado GMUnit and Road data.R', echo=F)
```

Take a peak at the boundary data
```{r}
head(Unitboundaries2)
```

Set to predictive analytics directory
```{r}
setwd("~/_code/colorado-dow/phase III - predictive analytics")
```

### Organize data
Will start by grouping all of the seasons together, and modeling the number of hunters per Year and Unit

Group Draw results data by Year and Unit
```{r}
COElkDraw_Unit <- summarise(group_by(COElkDrawAll,Year,Unit,Season),
                       Quota = sum(Orig_Quota,na.rm = T),
                       Drawn = sum(Chcs_Drawn,na.rm = T))
```

Appropriate field classes for model training
```{r}
COElkDraw_Unit$Year <- as.numeric(COElkDraw_Unit$Year)
```

Group Hunter data by Year and Unit
```{r}
COElkHunters_Unit <- summarise(group_by(COElkRifleAll,Year,Unit,Season),
                          Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T))

COElkHunters_Unit$Year <- as.numeric(COElkHunters_Unit$Year)
```

Join in Hunter and Draw data together
```{r}
COElkHunters_Unit <- left_join(COElkHunters_Unit, COElkDraw_Unit, by = c("Year","Unit","Season"))
```

Replace the draw data that don't have entries with 0
```{r}
COElkHunters_Unit$Drawn[is.na(COElkHunters_Unit$Drawn)] <- 0
COElkHunters_Unit$Quota[is.na(COElkHunters_Unit$Quota)] <- 0
```

Split into train and test sets. Will use 75% of the data to train on. 

```{r}
COElkHunters_Unit <- mutate(group_by(COElkHunters_Unit,Unit),
                       numentries = n())
COElkHunters_Unit <- filter(COElkHunters_Unit, numentries >= 3)
COElkHunters_Unit$UnitYearSeason <- paste(COElkHunters_Unit$Unit, COElkHunters_Unit$Year,COElkHunters_Unit$Season)

traindata2 <- COElkHunters_Unit %>% group_by(Unit) %>% sample_frac(size = .75, replace = F)
testdata2 <- COElkHunters_Unit[!COElkHunters_Unit$UnitYearSeason %in% traindata2$UnitYearSeason,]

COElkHunters_Unit <- select(COElkHunters_Unit, -UnitYearSeason, -numentries)

traindata2 <- select(traindata2, -UnitYearSeason, -numentries)
testdata2 <- select(testdata2, -UnitYearSeason, -numentries)
```

Save off for importing into AzureML
```{r}
write.csv(COElkHunters_Unit,file = "~/_code/colorado-dow/datasets/COElkHunters_Unit.csv",row.names = F)
```
### Data Visualization
notice that the number of hunters data is skewed.
```{r fig.width=10}
ggplot(COElkHunters_Unit, aes(Hunters)) + 
  geom_density() +
  xlab("Hunters in Unit") +
  ylab("Number of Units") +
  theme(axis.text.y = element_blank()) +
  labs(title="Distribution of Hunters in each Unit", subtitle="2006-2017", caption="source: cpw.state.co.us")

```


A general rule of thumb to consider is that skewed data whose ratio of the highest value to the 
lowest value is greater than 20 have significant skewness. Also, the skewness statistic can be 
used as a diagnostic. If the predictor distribution is roughly symmetric, the skewness values 
will be close to zero. As the distribution becomes more right skewed, the skewness statistic 
becomes larger. Similarly, as the distribution becomes more left skewed, the value becomes negative.
Replacing the data with the log, square root, or inverse may help to remove the skew.

Example of how BoxCox can redistribute the data
```{r}
preProcValues2 <- preProcess(as.data.frame(traindata2), method = "BoxCox")
trainBC <- predict(preProcValues2, as.data.frame(traindata2))
```

```{r fig.width=10}
ggplot(trainBC, aes(Hunters)) + 
  geom_density() +
  xlab("BoxCox Hunters in Unit") +
  ylab("Number of Units") +
  theme(axis.text.y = element_blank()) +
  labs(title="BoxCox Distribution of Hunters in each Unit", subtitle="2006-2017", caption="source: cpw.state.co.us")
```
caret has a preproccess function for correcting for skewness 'BoxCox', we will need to be sure to
look at using this function in the training models.

***
## Model Building

### Model Training Methods
Loop through possible methods, utilizing the quicker 'adaptive_cv' parameter search from caret.
Consider scripting this into AzureML to make it run much faster, though there is more setup and errors to 
control for

```{r}
quickmethods <- c("lm",'svmLinear',"svmRadial","knn","cubist","kknn","glm.nb")

step1_all_Unit <- NULL
for (imethod in quickmethods) {
  step1_Unit <- NULL
  start <- now()
  
  # if (imethod == "lm") {
  #   controlmethod <- "repeatedcv"
  # } else {controlmethod <- "adaptive_cv"}
  controlmethod <- "repeatedcv"
  fitControl <- trainControl(
    method = controlmethod,
    # search = 'random',
    number = 4,
    repeats = 4,
    allowParallel = TRUE,
    summaryFunction = defaultSummary)
  
  registerDoSEQ()
  registerDoMC(cores = 6)
  
  HuntersModel_1_Unit = train(Hunters ~ ., data = traindata2,
                         method = imethod,
                         tuneLength = 15,
                         trControl = fitControl)
  
  HuntersModel_1_Unit
  
  # measure performance
  predictdata <- predict(HuntersModel_1_Unit, testdata2)
  
  step1_Unit$method <- imethod
  step1_Unit$RMSE <- postResample(pred = predictdata, obs = testdata2$Hunters)[1]
  step1_Unit$duration <- now() - start
  step1_Unit <- as.data.frame(step1_Unit)
  step1_all_Unit <- rbind(step1_all_Unit,step1_Unit)
}
```
View Results, and compare to previous first models that did not expand to Seasons
```{r}
step1_all
step1_all_Unit
```
The performance RMSE metric is certainly improved when including the Seasonal grouping.
Lets take the best method, and see if it is visually reasonable while also charting without Seasons.

### Predictions using best Season model
Build model
```{r}
controlmethod <- "repeatedcv"
  fitControl <- trainControl(
    method = controlmethod,
    # search = 'random',
    number = 4,
    repeats = 4,
    allowParallel = TRUE,
    summaryFunction = defaultSummary)
  
registerDoSEQ()
registerDoMC(cores = 6)
  
HuntersModel_1_Unit = train(Hunters ~ ., data = COElkHunters_Unit,
                         method = "kknn",
                         # preProc = c("center","scale"), 
                         tuneLength = 15,
                         trControl = fitControl)
  
HuntersModel_1_Unit
```
Predict Hunter for next year, 2018
```{r}
# Get list of Units and Seasons that will have data
COElkHunters2018_Unit <- COElkHunters_Unit
COElkHunters2018_Unit$Unit_Season <- paste(COElkHunters2018_Unit$Unit,COElkHunters2018_Unit$Season)
COElkHunters2018_Unit <- as.data.frame(unique(COElkHunters2018_Unit$Unit_Season))
colnames(COElkHunters2018_Unit) <- "Unit_Season"
# Fill in missing Units and Seasons per unique Unit_Seasons
COElkHunters2018_Unit$Unit <- str_extract(COElkHunters2018_Unit$Unit_Season,"[:alnum:]+(?=[:blank:])")
COElkHunters2018_Unit$Season <- str_extract(COElkHunters2018_Unit$Unit_Season,"(?<=[:blank:])[:alnum:]+")
COElkHunters2018_Unit <- select(COElkHunters2018_Unit, -Unit_Season)
COElkHunters2018_Unit$Year <- 2018
# Draw data for 2018
COElkDraw_Unit2018 <- filter(COElkDraw_Unit,Year==2018)

# A left join will autofill missing draw data with NAs, but will retain the full list of Unit Seasons
COElkHunters2018_Unit <- left_join(COElkHunters2018_Unit,COElkDraw_Unit2018)

# Replace the draw data that don't have entries with 0
COElkHunters2018_Unit$Drawn[is.na(COElkHunters2018_Unit$Drawn)] <- 0
COElkHunters2018_Unit$Quota[is.na(COElkHunters2018_Unit$Quota)] <- 0

# Only use the fields that were included in the model
COElkHunters2018_Unit <- COElkHunters2018_Unit[, colnames(COElkHunters2018_Unit) %in% c("Unit","Season",HuntersModel_1_Unit$coefnames)]
# Use trained model to predict Hunters
COElkHunters2018_Unit$Hunters <- round(predict(HuntersModel_1_Unit, COElkHunters2018_Unit))

COElkHunters2018_Unit$Hunters[COElkHunters2018_Unit$Hunters<0] <- 0
```

Label and Join models together for comparisons
```{r}
# Load first model without Seasons
load("~/_code/colorado-dow/datasets/COElkHunters2018.RData")
Hunterscompare <- rbind.fill(COElkHunters,COElkHunters2018)
Hunterscompare$modeldata <- "Without Seasons"

Hunterscompare_Season <- rbind.fill(COElkHunters_Unit,COElkHunters2018_Unit)
Hunterscompare_Season <- summarise(group_by(Hunterscompare_Season,Year,Unit),
                                   Hunters = sum(Hunters))
Hunterscompare_Season$modeldata <- "Seasons"

Hunterscompare <- rbind.fill(Hunterscompare,Hunterscompare_Season)

```
```{r}
# Group Units
HunterscompareStatewide <- summarise(group_by(Hunterscompare,Year,modeldata),
                                   Hunters = sum(Hunters))
```

```{r fig.width=10}
ggplot(HunterscompareStatewide, aes(Year,Hunters,group=modeldata,fill=modeldata)) +
  geom_bar(position="dodge",stat="identity") +
  coord_cartesian(ylim = c(130000,155000)) +
  scale_fill_hc() +
  labs(title="Statewide Elk Hunters", caption="source: cpw.state.co.us")
```


#### Hunters Statewide by Season and Year
```{r fig.width=10}
Hunters_Season <- rbind.fill(COElkHunters_Unit,COElkHunters2018_Unit)
ggplot(Hunters_Season, aes(Year,Hunters,group=Season,fill=Season)) +
  geom_bar(position="dodge",stat="identity") +
  # coord_cartesian(ylim = c(130000,155000)) +
  scale_fill_hc() +
  labs(title="Statewide Elk Hunters", caption="source: cpw.state.co.us")
```

#### Hunters .... other charts?


## Keep going by creating models for hunters per year, unit and season?


```{r}
top_two_models <- top_n(step1_all,2,-RMSE)$method
```
### More Model Training Methods

Take the top two and determine some additonal methods to try by maximizing the Jaccard
dissimilarity between sets of models
```{r}
tag <- read.csv("tag_data.csv", row.names = 1)
tag <- as.matrix(tag)
```

Select only models for regression
```{r}
regModels <- tag[tag[,"Regression"] == 1,]

all <- 1:nrow(regModels)
dissimilarmethods_all <- NULL
for (itoptwo in 1:2) {
  ## Seed the analysis with the model of interest
  start <- grep(top_two_models[itoptwo], rownames(regModels), fixed = TRUE)
  pool <- all[all != start]
  
  ## Select 4 model models by maximizing the Jaccard
  ## dissimilarity between sets of models
  nextMods <- maxDissim(regModels[start,,drop = FALSE], 
                        regModels[pool, ], 
                        method = "Jaccard",
                        n = 4)
  
  rownames(regModels)[c(nextMods)]
  
  dissimilarmethods <- rownames(regModels)[nextMods]
  dissimilarmethods <- str_extract(string = dissimilarmethods,pattern = "[:alnum:]+(?=\\))")
  dissimilarmethods_all <- c(dissimilarmethods_all,dissimilarmethods)
}
```

Now we have 8 more methods to try in the same manner
```{r}
dissimilarmethods_all <- unique(dissimilarmethods_all)
dissimilarmethods_all
```
```{r}
for (imethod in dissimilarmethods_all) {
  step1 <- NULL
  start_timer <- now()[1]
  
  if (imethod == "lm") {
    controlmethod <- "repeatedcv"
  } else {controlmethod <- "adaptive_cv"}
  
  fitControl <- trainControl(
    method = controlmethod,
    # search = 'random',
    number = 4,
    repeats = 4,
    allowParallel = TRUE,
    summaryFunction = defaultSummary)
  
  registerDoSEQ()
  registerDoMC(cores = 6)
  
  HuntersModel_1 = train(Hunters ~ ., data = traindata,
                         method = imethod,
                         preProc = c("center","scale"), 
                         tuneLength = 15,
                         trControl = fitControl)
  
  HuntersModel_1
  
  # measure performance
  predictdata <- predict(HuntersModel_1, testdata)
  
  step1$method <- imethod
  step1$RMSE <- postResample(pred = predictdata, obs = testdata$Hunters)[1]
  step1$duration <- now()[1] - start_timer[1]
  step1 <- as.data.frame(step1)
  step1_all <- rbind(step1_all,step1)
}
```

```{r}
step1_all
```

### Preprocessing on Top Modeling Methods
Now lets work on some refined tuning on the top methods
Any valuable preprocessing steps?
```{r}
preprocessfunctions <- c("BoxCox", "YeoJohnson", "expoTrans", "center", "scale", "range", "knnImpute", "bagImpute", "medianImpute", "pca", "ica", "spatialSign", "corr", "zv", "nzv")
topmethods <- top_n(step1_all,2,-RMSE)$method

fitControl <- trainControl(
  method = "adaptive_cv", #repeatedcv, 
  search = 'random',
  number = 10, #4
  repeats = 10, #10
  # classProbs = TRUE,
  # savePred = TRUE,
  allowParallel = TRUE,
  summaryFunction = defaultSummary)

PPperformance_all <- NULL
PPperformance <- NULL
for (imethod in topmethods) {
  for (ipreprocess in preprocessfunctions) {
    registerDoSEQ()
    registerDoMC(cores = 6)
    
    PreProcessModel = train(Hunters ~ ., data = traindata,
                         method = imethod,
                         preProc = ipreprocess, 
                         #tuneLength = 10,
                         #tuneGrid = kknnTuneGrid,
                         trControl = fitControl)
    
    print(PreProcessModel)
    
    # check performance
    predictdata <- predict(PreProcessModel, testdata)
    
    PPperformance$method <- imethod
    PPperformance$preprocess <- ipreprocess
    PPperformance$RMSE <- postResample(pred = predictdata, obs = testdata$Hunters)[1]
    PPperformance <- as.data.frame(PPperformance)
    PPperformance_all <- rbind(PPperformance_all,PPperformance)
  }
}
# Some of the models were loaded into AzureML and processed there.
# Output from AzureML
# [ModuleOutput]          method preprocess     RMSE
# [ModuleOutput] RMSE       kknn     BoxCox 130.7939
# [ModuleOutput] RMSE1      kknn YeoJohnson 130.9600
# [ModuleOutput] RMSE2      kknn     center 130.7331
# [ModuleOutput] RMSE3      kknn      scale 130.1818
# [ModuleOutput] RMSE4      kknn        pca 130.2071
# [ModuleOutput] RMSE5 svmRadial     BoxCox 154.0898
# [ModuleOutput] RMSE6 svmRadial YeoJohnson 169.9816
# [ModuleOutput] RMSE7 svmRadial     center 154.1891
# [ModuleOutput] RMSE8 svmRadial      scale 154.1000
# [ModuleOutput] RMSE9 svmRadial        pca 164.0881
# svmRadial and kknn don't perform better with any of the preprocessing functions in place
```
```{r}
PPperformance_all
```

### Model Predictors
Now we can review the predictors, there are only a few fields so I will manually test performance
while excluding each of them to monitor their importance.
Some of our fields are instinctively required (Year, Unit)
```{r}
Predictors <- c("Quota","Drawn")
Predictorperformance_all <- NULL
Predictorperformance <- NULL
for (imethod in topmethods) {
  for (ipredictor in Predictors) {
    registerDoSEQ()
    registerDoMC(cores = 6)
    
    PredictorModel = train(Hunters ~ ., data = select(traindata,-ipredictor),
                            method = imethod,
                            tuneLength = 15,
                            trControl = fitControl)
    
    print(PredictorModel)
    
    # check performance
    predictdata <- predict(PredictorModel, testdata)
    
    Predictorperformance$method <- imethod
    Predictorperformance$missing_predictor <- ipredictor
    Predictorperformance$RMSE <- postResample(pred = predictdata, obs = testdata$Hunters)[1]
    Predictorperformance <- as.data.frame(Predictorperformance)
    Predictorperformance_all <- rbind(Predictorperformance_all,Predictorperformance)
  }
}
```

```{r}
Predictorperformance_all
```


svMRadial will perform better with all of the predictors, while kknn performs
better with only Unit and Year fields

Use above information to test out various combinations of preprocessing and predictor sets

##### kknn
kknn without Quota and Drawn
```{r}
fitControl <- trainControl(
  method = "adaptive_cv", #repeatedcv, 
  search = 'random',
  number = 10, #4
  repeats = 10, #10
  # classProbs = TRUE,
  # savePred = TRUE,
  allowParallel = TRUE,
  summaryFunction = defaultSummary)

registerDoSEQ()
registerDoMC(cores = 6)

kknnModel = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
                  method = "kknn",
                  tuneLength = 75,
                  trControl = fitControl)

```
```{r}
kknnModel
```

Best RMSE
```{r}
# not sure why caret is selecting parameters with higher RMSE, lets select manually
RSMEkknn <- filter(kknnModel$results, RMSE == min(RMSE))
RSMEkknn$kernel <- as.character(RSMEkknn$kernel)
RSMEkknn
```

### Model Tuning
run again with a tune grid
```{r}
kknnTuneGrid <- data.frame(kmax = c(RSMEkknn$kmax,RSMEkknn$kmax,RSMEkknn$kmax,RSMEkknn$kmax,RSMEkknn$kmax),
                           distance = c(RSMEkknn$distance*.7,RSMEkknn$distance*.9,RSMEkknn$distance,RSMEkknn$distance*1.1,RSMEkknn$distance*1.3),
                           kernel = c(RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel))

fitControl <- trainControl(
  method = "repeatedcv", #repeatedcv, 
  number = 10, #4
  repeats = 10, #10
  allowParallel = TRUE,
  summaryFunction = defaultSummary)

registerDoSEQ()
registerDoMC(cores = 6)

kknnGridModel = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
                  method = "kknn",
                  tuneGrid = kknnTuneGrid,
                  trControl = fitControl)
```
```{r}
kknnGridModel
```


Best RMSE
```{r}
RSMEkknn <- filter(kknnGridModel$results, RMSE == min(RMSE))
RSMEkknn$kernel <- as.character(RSMEkknn$kernel)
```

run again with a tune grid
```{r}
kknnTuneGrid2 <- data.frame(kmax = c(RSMEkknn$kmax*.7,RSMEkknn$kmax*.9,RSMEkknn$kmax,RSMEkknn$kmax*1.1,RSMEkknn$kmax*1.3),
                           distance = c(RSMEkknn$distance,RSMEkknn$distance,RSMEkknn$distance,RSMEkknn$distance,RSMEkknn$distance),
                           kernel = c(RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel,RSMEkknn$kernel))

registerDoSEQ()
registerDoMC(cores = 6)

kknnGridModel2 = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
                      method = "kknn",
                      tuneGrid = kknnTuneGrid2,
                      trControl = fitControl)

```

```{r}
kknnGridModel2
```


One more time on final parameter (kernel)
Best RMSE
```{r}
RSMEkknn <- filter(kknnGridModel2$results, RMSE == min(RMSE))[1,]
kernels <- levels(kknnModel$results$kernel)
```

run again with a tune grid
```{r}
kknnTuneGrid3 <- data.frame(kmax = rep(465.0,8),
                            distance = rep(0.1395586,8),
                            kernel = kernels)

registerDoSEQ()
registerDoMC(cores = 6)

kknnGridModel3 = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
                       method = "kknn",
                       tuneGrid = kknnTuneGrid3,
                       trControl = fitControl)

```

```{r}
kknnGridModel3
```

```{r}
RSMEkknn <- filter(kknnGridModel3$results, RMSE == min(RMSE))
```

Best RMSE for kknn thus far
```{r}
RSMEkknn <- filter(kknnModel$results, RMSE == min(RMSE))
```

Work thru some resampling methods with best kknn params
```{r}
kknnTuneGrid4 <- data.frame(kmax = RSMEkknn$kmax,
                            distance = RSMEkknn$distance,
                            kernel = as.character(RSMEkknn$kernel))

trainmethods <- c("boot", "boot632", "optimism_boot", "boot_all", "cv", "repeatedcv", "LOOCV", "LGOCV", "none")
trainmethodperformance_all <- NULL
for (itrainmethod in trainmethods) {
  trainmethodperformance <- NULL
  fitControl <- trainControl(
    method = itrainmethod,
    number = 10, #4
    repeats = 10, #10
    allowParallel = TRUE,
    summaryFunction = defaultSummary)
  
  registerDoSEQ()
  registerDoMC(cores = 6)
  
  kknnTrainModel = train(Hunters ~ ., data = select(COElkHunters,-Quota, -Drawn),
                         method = "kknn",
                         tuneGrid = kknnTuneGrid4,
                         trControl = fitControl)
  
  print(kknnTrainModel)
  trainmethodperformance <- filter(kknnTrainModel$results, RMSE == min(RMSE))
  trainmethodperformance$trainmethod <- itrainmethod
  trainmethodperformance_all <- rbind.fill(trainmethodperformance_all,trainmethodperformance)
}
```
```{r}
trainmethodperformance_all
```


```{r}
fitControl <- trainControl(
  method = "optimism_boot",
  number = 10, #4
  allowParallel = TRUE,
  summaryFunction = defaultSummary)

kknnFinalTrainModel = train(Hunters ~ ., data = COElkHunters,
                       method = "kknn",
                       tuneGrid = kknnTuneGrid4,
                       trControl = fitControl)

```

save off for future loading
```{r}
save(kknnFinalTrainModel, file = "~/_code/colorado-dow/datasets/kknnFinalTrainModel.RData")
```
## Model Testing
back to train vs test data for one more performance measure and chart... even though
for future data we will use the final trained model
```{r}
kknnTrainModel = train(Hunters ~ ., data = traindata,
                            method = "kknn",
                            tuneGrid = kknnTuneGrid4,
                            trControl = fitControl)
```

check performance
```{r}
predictdata <- predict(kknnTrainModel, testdata)

postResample(pred = predictdata, obs = testdata$Hunters)
```

Chart performance of predicted
```{r}
chartperformance <- data.frame(predicted = predictdata, observed = testdata$Hunters)
```

```{r fig.width=10}
ggplot(chartperformance, aes(predicted,observed)) +
  geom_point() +
  labs(title="Performance of Number of Hunters Prediction", caption="source: cpw.state.co.us")
```



## SVM 
Output from AzureML
[ModuleOutput] Support Vector Machines with Radial Basis Function Kernel 
[ModuleOutput] 
[ModuleOutput] 1540 samples
[ModuleOutput]    4 predictors
[ModuleOutput] 
[ModuleOutput] No pre-processing
[ModuleOutput] Resampling: Cross-Validated (10 fold, repeated 10 times) 
[ModuleOutput] 
[ModuleOutput] Summary of sample sizes: 1386, 1386, 1387, 1387, 1385, 1385, ... 
[ModuleOutput] 
[ModuleOutput] Resampling results across tuning parameters:
[ModuleOutput] 
[ModuleOutput]   C        RMSE  Rsquared  RMSE SD  Rsquared SD
[ModuleOutput]   0.25     276   0.946     30.6     0.00822    
[ModuleOutput]   0.5      203   0.96      21.4     0.00754    
[ModuleOutput]   1        180   0.965     17.7     0.00671    
[ModuleOutput]   2        168   0.969     15.7     0.00614    
[ModuleOutput]   4        158   0.972     14.9     0.0055     
[ModuleOutput]   8        150   0.975     14.7     0.00506    
[ModuleOutput]   16       146   0.976     14.7     0.00481    
[ModuleOutput]   32       144   0.976     14.7     0.00477    
[ModuleOutput]   64       143   0.977     14.6     0.00474    
[ModuleOutput]   128      140   0.977     14.3     0.00469    
[ModuleOutput]   256      139   0.978     15       0.00485    
[ModuleOutput]   512      137   0.978     14.9     0.00484    
[ModuleOutput]   1020     136   0.979     15.3     0.00494    
[ModuleOutput]   2050     135   0.979     15.6     0.00513    
[ModuleOutput]   4100     136   0.979     15.5     0.0051     
[ModuleOutput]   8190     137   0.978     15.7     0.00518    
[ModuleOutput]   16400    139   0.978     16.5     0.00551    
[ModuleOutput]   32800    141   0.977     17.6     0.006      
[ModuleOutput]   65500    145   0.976     19.4     0.00659    
[ModuleOutput]   131000   151   0.974     20.8     0.00718    
[ModuleOutput]   262000   161   0.97      27.2     0.0104     
[ModuleOutput]   524000   478   0.8       325      0.172      
[ModuleOutput]   1050000  1180  0.5       1010     0.204      
[ModuleOutput]   2100000  3240  0.148     2260     0.117      
[ModuleOutput]   4190000  6000  0.0604    6400     0.0527     
[ModuleOutput] 
[ModuleOutput] Tuning parameter 'sigma' was held constant at a value of 0.0037653
[ModuleOutput] RMSE was used to select the optimal model using  the smallest value.
[ModuleOutput] The final values used for the model were sigma = 0.00377 and C = 2048. 


# run again with a tune grid
```{r}
svmRadTuneGrid <- data.frame(.sigma = c(0.0037653,0.0037653,0.0037653,0.0037653,0.0037653),
                            .C = c(2048*.7,2048*.9,2048,2048*1.1,2048*1.3))

fitControl <- trainControl(
  method = "repeatedcv", #repeatedcv, 
  number = 10, #4
  repeats = 10, #10
  allowParallel = TRUE,
  summaryFunction = defaultSummary)

registerDoSEQ()
registerDoMC(cores = 6)

svmRadGridModel = train(Hunters ~ ., data = COElkHunters,
                      method = "svmRadial",
                      tuneGrid = svmRadTuneGrid,
                      trControl = fitControl)
```

```{r}
svmRadGridModel
```


Best RMSE, not sure why caret is selecting parameters with higher RMSE, lets select manually
```{r}
RSMEsvmRad <- filter(svmRadGridModel$results, RMSE == min(RMSE))
```

run again with a tune grid
```{r}
svmRadTuneGrid2 <- data.frame(.sigma = c(RSMEsvmRad$sigma*.7,RSMEsvmRad$sigma*.9,RSMEsvmRad$sigma,RSMEsvmRad$sigma*1.1,RSMEsvmRad$sigma*1.3),
                             .C = c(RSMEsvmRad$C,RSMEsvmRad$C,RSMEsvmRad$C,RSMEsvmRad$C,RSMEsvmRad$C))

registerDoSEQ()
registerDoMC(cores = 6)

svmRadGridModel2 = train(Hunters ~ ., data = COElkHunters,
                        method = "svmRadial",
                        tuneGrid = svmRadTuneGrid2,
                        trControl = fitControl)
```

```{r}
svmRadGridModel2
```

```{r}
RSMEsvmRad <- filter(svmRadGridModel2$results, RMSE == min(RMSE))
```

Work thru some resampling methods with best kknn params
```{r}
svmRadTuneGrid3 <- data.frame(.sigma = RSMEsvmRad$sigma,
                            .C = RSMEsvmRad$C)

trainmethods <- c("boot", "boot632", "optimism_boot", "cv", "repeatedcv", "LOOCV", "LGOCV", "none")
trainmethodperformance_all <- NULL
for (itrainmethod in trainmethods) {
  trainmethodperformance <- NULL
  fitControl <- trainControl(
    method = itrainmethod,
    number = 10, #4
    repeats = 10, #10
    allowParallel = TRUE,
    summaryFunction = defaultSummary)
  
  registerDoSEQ()
  registerDoMC(cores = 6)
  
  svmRadTrainModel = train(Hunters ~ ., data = COElkHunters,
                         method = "svmRadial",
                         tuneGrid = svmRadTuneGrid3,
                         trControl = fitControl)
  
  print(svmRadTrainModel)
  trainmethodperformance <- filter(svmRadTrainModel$results, RMSE == min(RMSE))
  trainmethodperformance$trainmethod <- itrainmethod
  trainmethodperformance_all <- rbind.fill(trainmethodperformance_all,trainmethodperformance)
}
```
```{r}
trainmethodperformance_all
```

```{r}
fitControl <- trainControl(
  method = "optimism_boot",
  number = 10, #4
  allowParallel = TRUE,
  summaryFunction = defaultSummary)

svmRadFinalTrainModel = train(Hunters ~ ., data = COElkHunters,
                            method = "svmRadial",
                            tuneGrid = svmRadTuneGrid3,
                            trControl = fitControl)

```

save off for future loading
```{r}
save(svmRadFinalTrainModel, file = "~/_code/colorado-dow/datasets/svmRadFinalTrainModel.RData")
```

back to train vs test data for one more performance measure and chart... even though
for future data we will use the final trained model
```{r}
svmRadTrainModel = train(Hunters ~ ., data = traindata,
                       method = "svmRadial",
                       tuneGrid = svmRadTuneGrid3,
                       trControl = fitControl)
```

check performance
```{r}
predictdata <- predict(svmRadTrainModel, testdata)
postResample(pred = predictdata, obs = testdata$Hunters)
```

Chart performance of predicted
```{r fig.width=10}
chartperformance <- data.frame(predicted = predictdata, observed = testdata$Hunters)
ggplot(chartperformance, aes(predicted,observed)) +
  geom_point() +
  labs(title="Performance of Number of Hunters Prediction", caption="source: cpw.state.co.us")
```


kknn performed better than svmRadial RMSE=130 vs 154
```{r}
FinalHuntersmodel <- kknnFinalTrainModel
# FinalHuntersmodel <- svmRadFinalTrainModel
```
```{r}
save(FinalHuntersmodel, file = "~/_code/colorado-dow/datasets/FinalHuntersmodel.RData")
```

Use the 2018 Draw data to predict the number of hunters in 2018
```{r}
COElkDraw2018 <- filter(COElkDraw, Year == 2018)
COElkHunters2018 <- COElkDraw2018[, colnames(COElkDraw2018) %in% c("Unit",FinalHuntersmodel$coefnames)]

COElkHunters2018 <- as.data.frame(unique(COElkHunters$Unit))
colnames(COElkHunters2018) <- "Unit"
COElkHunters2018$Year <- 2018
COElkHunters2018 <- left_join(COElkHunters2018,filter(COElkDraw,Year==2018))
# Replace the draw data that don't have entries with 0
COElkHunters2018$Drawn[is.na(COElkHunters2018$Drawn)] <- 0
COElkHunters2018$Quota[is.na(COElkHunters2018$Quota)] <- 0

COElkHunters2018 <- COElkHunters2018[, colnames(COElkHunters2018) %in% c("Unit",FinalHuntersmodel$coefnames)]

COElkHunters2018$Hunters <- round(predict(FinalHuntersmodel, COElkHunters2018))

COElkHunters2018$Hunters[COElkHunters2018$Hunters<0] <- 0
```

Save off so we don't have to recreate the model everytime we want the results
```{r}
save(COElkHunters2018,file="COElkHunters2018.RData")
```

***
## Total Elk Harvest
### Statewide
Group seasons
```{r}
COElkHuntersStatewide <- summarise(group_by(COElkRifleAll,Year,Unit),
                                  Hunters = sum(c(Hunters.Antlered,Hunters.Antlerless,Hunters.Either),na.rm = T))
COElkHunters2018b <- COElkHunters2018
# COElkHunters2018b$Year <- as.character(COElkHunters2018b$Year)

# Join 2018 to historic data
COElkHuntersAll <- rbind.fill(COElkHuntersStatewide,COElkHunters2018b)

# Group Units
COElkHuntersStatewide <- summarise(group_by(COElkHuntersAll,Year),
                                   Hunters = sum(Hunters))
```

```{r fig.width=10}
ggplot(COElkHuntersStatewide, aes(Year,Hunters)) +
  geom_bar(stat="identity",fill=ggthemes_data$hc$palettes$default[2]) +
  coord_cartesian(ylim = c(130000,155000)) +
  labs(title="Statewide Elk Hunters", caption="source: cpw.state.co.us")
```


> TODO commentary

***

### Hunters by Unit
I'd like to know where the hunters are distributed across the state.

Next year's data
```{r}
Year2018 <- filter(COElkHuntersAll, Year == "2018")
HunterstoPlot <- left_join(Unitboundaries2,Year2018, by=c("Unit"))
```
```{r fig.width=10, fig.height=8.46}
ggplot(HunterstoPlot, aes(long, lat, group = group)) + 
  geom_polygon(aes(fill = Hunters),colour = "grey50", size = .2) + #Unit boundaries
  geom_path(data = COroads,aes(x = long, y = lat, group = group), color="#3878C7",size=2) + #Roads
  geom_text(data=data_centroids,aes(x=longitude,y=latitude,label = Unit),size=3) + #Unit labels
  scale_fill_distiller(palette = "Oranges",direction = 1,na.value = 'light grey') +
  xlab("") + ylab("") +
  labs(title="Predicted 2018 Colorado Elk Hunters", caption="source: cpw.state.co.us")
```


> TODO - commentary

***

### Year to Year Hunter Trends
Create a png of each year
```{r}
icounter <- 0
for (imap in unique(COElkHuntersAll$Year)){
  # Colorado aspect ratio = 1087w x 800h -> 1.35875
  # Use trial and error to determine which width and height to define for png files that will retain the correct aspect ratio
  png(file=paste("HuntersMap",imap,".png"), width=948, height=700)
  yearplot <- filter(COElkHuntersAll, Year == imap)
  HunterstoPlot <- left_join(Unitboundaries2,yearplot, by=c("Unit"))
  p1 <- ggplot(HunterstoPlot, aes(long, lat, group = group)) + 
    geom_polygon(aes(fill = Hunters),colour = "grey50", size = .2) + #Unit boundaries
    geom_path(data = COroads,aes(x = long, y = lat, group = group), color="#3878C7",size=2) + #Roads
    geom_text(data=data_centroids,aes(x=longitude,y=latitude,label = Unit),size=5) + #Unit labels
    scale_fill_distiller(palette = "Oranges",
                         direction = 1,
                         na.value = 'light grey',
                         limits = c(0,max(COElkHuntersAll$Hunters))) + #fix so each year chart has same color breaks
    xlab("") + ylab("") +
    theme(plot.title=element_text(hjust = .5)) +
    theme(plot.subtitle=element_text(hjust = icounter/length(unique(COElkHuntersAll$Year)))) +
    labs(title="Colorado Elk Hunters", subtitle=imap, caption="source: cpw.state.co.us")
  plot(p1)
  dev.off()
  icounter <- icounter + 1
}
```
Convert the .png files to one .gif file using ImageMagick. 
```{r}
system("convert -delay 150 *.png HuntersmapPred.gif")
```

![](HuntersmapPred.gif)
> TODO - commentary

Remove the .png files
```{r}
file.remove(list.files(pattern=".png"))
```

***
### Number of Hunters Rank of the Units
Would also be beneficial to rank each unit so I can reference later. In this case
average the number of hunters of the last few years
```{r}
HunterRank2018 <- filter(COElkHuntersAll, as.numeric(Year) == 2018)
HunterRank2018 <- summarise(group_by(HunterRank2018,Unit),
                             Hunters = mean(Hunters,na.rm = T))
HunterRank2018$HuntersRank = rank(-HunterRank2018$Hunters)

HunterRank2018 <- filter(HunterRank2018, HuntersRank <= 50) # top 50 units
# In order for the chart to retain the order of the rows, the X axis variable (i.e. the categories) has to be converted into a factor.
HunterRank2018 <- HunterRank2018[order(-HunterRank2018$Hunters), ]  # sort
HunterRank2018$Unit <- factor(HunterRank2018$Unit, levels = HunterRank2018$Unit)  # to retain the order in plot.
```

Lollipop Chart
```{r}
ggplot(HunterRank2018, aes(x=Unit, y=Hunters)) + 
  geom_point(size=3) + 
  geom_segment(aes(x=Unit, 
                   xend=Unit, 
                   y=0, 
                   yend=Hunters)) + 
  labs(title="Predicted Elk Hunters 2018\nTop 50 Units", subtitle="Hunters by Unit", caption="source: cpw.state.co.us")
```

> TODO - commentary

***
## Conclusion
> TODO


